利用条件生成对抗网络对电价情景进行概率模拟

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Viktor Walter , Andreas Wagner
{"title":"利用条件生成对抗网络对电价情景进行概率模拟","authors":"Viktor Walter ,&nbsp;Andreas Wagner","doi":"10.1016/j.egyai.2024.100422","DOIUrl":null,"url":null,"abstract":"<div><p>A novel approach for generative time series simulation of electricity price scenarios is presented. A “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) generates short-term electricity price scenarios. In particular, the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets. The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast. The data cover a 3-year period from 2020 to 2023. Our empirical study is conducted on the EPEX SPOT market in Europe. An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange. The model uses multivariate time series as input factors, consisting of point forecasts of electricity prices and fundamental data on generation and load profiles. The architecture of a TSS-CGAN is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The model is evaluated using qualitative and quantitative criteria. For the evaluation, 10,000 simulations of a test period are carried out. Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately. The quantitative analysis includes common error metric, compared to benchmark models, like DeepAR, Prophet and Temporal Fusion Transformer, the examination of the quantile ranges, the error distribution and a sensitivity analysis. The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50% and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000880/pdfft?md5=68acafc3eee4796c88abd4e8470f8783&pid=1-s2.0-S2666546824000880-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Probabilistic simulation of electricity price scenarios using Conditional Generative Adversarial Networks\",\"authors\":\"Viktor Walter ,&nbsp;Andreas Wagner\",\"doi\":\"10.1016/j.egyai.2024.100422\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A novel approach for generative time series simulation of electricity price scenarios is presented. A “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) generates short-term electricity price scenarios. In particular, the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets. The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast. The data cover a 3-year period from 2020 to 2023. Our empirical study is conducted on the EPEX SPOT market in Europe. An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange. The model uses multivariate time series as input factors, consisting of point forecasts of electricity prices and fundamental data on generation and load profiles. The architecture of a TSS-CGAN is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The model is evaluated using qualitative and quantitative criteria. For the evaluation, 10,000 simulations of a test period are carried out. Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately. The quantitative analysis includes common error metric, compared to benchmark models, like DeepAR, Prophet and Temporal Fusion Transformer, the examination of the quantile ranges, the error distribution and a sensitivity analysis. The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50% and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.</p></div>\",\"PeriodicalId\":34138,\"journal\":{\"name\":\"Energy and AI\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000880/pdfft?md5=68acafc3eee4796c88abd4e8470f8783&pid=1-s2.0-S2666546824000880-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000880\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666546824000880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种新颖的电价情景时间序列生成模拟方法。一种 "时间序列模拟条件生成对抗网络"(TSS-CGAN)可生成短期电价情景。特别是,该网络能够生成与电力市场预期行为相对应的 24 维输出向量。该模型可以替代金融数学中的典型方法,如统计因子模型,来模拟给定预测周围的价格分布。数据涵盖 2020 年至 2023 年的三年期。我们的实证研究是在欧洲 EPEX SPOT 市场上进行的。电价情景包括 EPEX SPOT 电力交易所日前拍卖的每小时合同价格。该模型使用多变量时间序列作为输入因素,其中包括电价点预测以及发电和负荷曲线的基本数据。TSS-CGAN 的结构基于条件生成对抗网络与一维卷积神经网络和双向长短期记忆相结合的思想。该模型采用定性和定量标准进行评估。在评估过程中,对一个测试期进行了 10,000 次模拟。定性标准是该模型是否遵循某些电力市场特有的规律性并对其进行充分描述。定量分析包括与 DeepAR、Prophet 和 Temporal Fusion Transformer 等基准模型相比的常见误差度量、量化范围检查、误差分布和敏感性分析。结果表明,TSS-CGAN 的性能优于 DeepAR 等基准模型,其连续排名概率得分降低了 50%,并考虑了市场的具体情况,如波动能源的生产,并能对相应变量的变化做出正确反应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probabilistic simulation of electricity price scenarios using Conditional Generative Adversarial Networks

A novel approach for generative time series simulation of electricity price scenarios is presented. A “Time Series Simulation Conditional Generative Adversarial Network” (TSS-CGAN) generates short-term electricity price scenarios. In particular, the network is capable of generating a 24-dimensional output vector that corresponds to the expected behavior of electricity markets. The model can replace typical approaches from financial mathematics like statistical factor models to model the price distribution around a given forecast. The data cover a 3-year period from 2020 to 2023. Our empirical study is conducted on the EPEX SPOT market in Europe. An electricity price scenario includes the prices of the hourly contracts of a day-ahead auction at the EPEX SPOT power exchange. The model uses multivariate time series as input factors, consisting of point forecasts of electricity prices and fundamental data on generation and load profiles. The architecture of a TSS-CGAN is based on the idea of Conditional Generative Adversarial Networks combined with 1D Convolutional Neural Networks and Bidirectional Long Short-Term Memory. The model is evaluated using qualitative and quantitative criteria. For the evaluation, 10,000 simulations of a test period are carried out. Qualitative criteria are whether the model follows certain electricity market-specific regularities and depicts them adequately. The quantitative analysis includes common error metric, compared to benchmark models, like DeepAR, Prophet and Temporal Fusion Transformer, the examination of the quantile ranges, the error distribution and a sensitivity analysis. The results show that the TSS-CGAN outperforms benchmark models such as DeepAR by reducing the continuous ranked probability score by 50% and considers market-specific circumstances such as the production of fluctuating energies and reacts correctly to changes in the corresponding variables.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信